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Matthias Dischinger:
Profit Shifting by Multinationals: Indirect Evidence fromEuropean Micro Data
Munich Discussion Paper No. 2007-30
Department of EconomicsUniversity of Munich
Volkswirtschaftliche FakultätLudwig-Maximilians-Universität München
Online at http://epub.ub.uni-muenchen.de/2029/
Profit Shifting by Multinationals: Indirect Evidence
from European Micro Data
Matthias Dischinger∗
Department of Economics, University of Munich
Version: September 15, 2007
Abstract
We provide indirect empirical evidence of profit shifting behavior by multi-
national enterprises. This issue is analyzed in an econometric panel study for
the years 1995 to 2005 and additionally in a cross–section for 2004 using a
large micro database of European subsidiaries of multinationals (AMADEUS)
which includes detailed balance sheet items. Our results show a decrease in the
unconsolidated pre–tax profits of an affiliated company of approximately 7% if
the difference in the statutory corporate tax rate of this affiliate to its parent
increases by 10 percentage points. Various robustness checks support our profit
shifting evidence. Furthermore, the results suggest an overall shift of profits out
of the European Union. In addition, we provide evidence that a higher par-
ent’s ownership share of its subsidiary leads to intensified profit shifting behavior.
JEL classification: H25, H26, F23, C33
Keywords: corporate taxation, multinational enterprise, tax planning, profit
shifting, micro level data, panel econometrics
∗Seminar for Economic Policy, Department of Economics, University of Munich, Akademiestr. 1/II,
80799 Munich, Germany, phone: +49-89-2180 2279, e-mail: [email protected].
I am grateful to Andreas Haufler, Simon Loretz, Nadine Riedel, Johannes Rincke, Marco Runkel
and Christian Traxler for helpful and constructive comments. Furthermore, I would like to thank
participants of the Public Economics Seminar at the University of Munich as well as session members
of the 63rd Congress of the International Institute of Public Finance (IIPF) at the University of
Warwick. Renate Stobel and Ulrich Glogowsky have provided valuable research assistance. Financial
support by the German Research Foundation (DFG) is gratefully acknowledged.
1
1 Introduction
The current corporate tax system in the European single market provides multinational
enterprises (MNEs) with the opportunity to avoid taxation due to different national tax
rates and separate taxation of profits of each foreign subsidiary (separate accounting).
It is of fundamental importance for governments, especially those in high tax coun-
tries, to protect their shrinking national corporate tax revenue against various kinds
of profit shifting activities.1 On the other hand, shifting of profits likewise induces
substantial costs for MNEs. These include implementation costs and efficiency costs
due to internal management distortions, as well as compliance costs due to auditing
by regulating authorities. The auditing in turn constitute costs and efficiency losses
for governments. The current proposal of the European Commission (2001) aims to
reduce this tax avoidance behavior by consolidating all corporate tax bases of a MNE
and reallocating this profit to the different subsidiary locations according to a specific
formula (formula apportionment).2 This paper empirically analyzes to what extent
multinationals actually shift profits to low tax countries or out of high tax countries in
the enlarged European Union (EU). These results can be compared to those that have
been found in the extensive literature analyzing related issues with US data. There
are two reasons why profit shifting may even be higher in Europe. On the one hand,
there are large differences in corporate tax rates in the EU, even among neighboring
states. On the other hand, most EU countries employ the exemption system for taxing
corporate profits, under which international tax differentials in the source country of
the investment are directly relevant for the after–tax profits of companies.3
The empirical literature on profit shifting is large but it focuses mainly on US data
(see Hines (1997), Hines (1999), and Devereux (2006) for comprehensive surveys). Most
studies provide indirect evidence as data on internal transactions is limited even in
1Basically, three methods of shifting profits can be distinguished. The first is charging intercompany
intermediate goods for a higher or a lower than the arm’s length price (transfer pricing). Secondly,
overhead costs, e.g. for R&D or headquarter services, can be allocated strategically to subsidiaries in
different countries in order to bias their pre–tax profits. Third, MNEs can shift profits via the channel
of intercompany financial transactions, for instance by granting advantageous interest rates for loans.
2The potential effects of such an EU tax system of formula apportionment on corporate tax revenues
in each member state is studied by Devereux and Loretz (2007) using a large sample of international
micro data.
3The system of foreign tax credit applied in the US aims to equalize international differences in
corporate tax rates through compensating supplementary taxation of foreign source income in the
parent country. This should induce fewer incentives to shift profits, other things being equal.
2
the US. The standard method used in the literature tries to explain differences in
(unconsolidated) pre–tax profits of affiliated companies by the statutory corporate tax
rate which is effective at the affiliate’s location, while controlling for firm and country
characteristics. Grubert and Mutti (1991) and Hines and Rice (1994), for example,
perform this with aggregate data on affiliates by country, whereas e.g. Harris, Morck,
Slemrod, and Yeung (1993) as well as Collins, Kemsley, and Lang (1998) use firm–level
data. To give indirect evidence for profit shifting, a more precise explanatory variable
for the variance of pre–tax profits is the statutory tax rate difference of an affiliated firm
to its next foreign affiliated company if there is only one (this is, e.g. the foreign parent
if the respective affiliate has no further foreign subsidiaries). In our paper, we use this
tax differential, which gives the MNE the direct incentive to shift profits between these
two affiliates.
So far, only a few published studies yield direct evidence of profit shifting by using
affiliate level data on intercompany transfer prices (Clausing (2003), Swenson (2001),
and Bernard, Jensen, and Schott (2006)). A paper that identifies profit shifting via
the allocation of overhead costs is Grubert (2003).4 The channel of financial transac-
tions is instead rather unexplored.5 Evidence for profit shifting with European data
is still rare. Weichenrieder (2007) confirms profit shifting into and out of Germany
with German panel FDI–data (MiDi database). With the same database, Overesch
(2006) demonstrates for German MNEs a negative impact of the tax rate on the size
of intercompany sales (transfer pricing channel). Huizinga and Laeven (2005), with a
methodology very close to Hines and Rice (1994), perform a cross–section analysis with
affiliate level data (AMADEUS) to provide evidence for profit shifting within European
MNEs by explaining variations in earnings before interest and taxes (EBIT) with tax
differentials, among other variables.
This paper provides indirect evidence of profit shifting using panel regressions for the
years 1995–2005 while controlling for fixed firm effects. We provide a contribution to
4Grubert (2003) provides evidence that subsidiaries in countries with a relatively low or high tax
rate engage in a significantly larger volume of intercompany transactions. These transactions consist
mainly of immaterial, R&D–intensive goods (e.g. payments for royalties or patents).
5A study by Mintz and Smart (2004) finds some evidence for the use of this channel, as comparing
their results with data from firms with a solely national organizational form which cannot shift profits
internationally. Papers by Mintz and Weichenrieder (2005) and Ramb and Weichenrieder (2005), which
are primarily focusing on the influence of corporate tax rates on the level of debt financing using micro
data from Germany, allow similar inferences by comparing tax induced intercompany borrowing with
external debt financing. More robust evidence is found in a recent paper by Buettner and Wamser
(2007) who can identify the use of intercompany loans solely for profit shifting purposes.
3
this literature using European micro data with the AMADEUS database. Our sample
of MNEs consists of profit–making affiliated firms which are owned by a foreign parent
with an ownership share of at least 90%. Our results show a robust and significant
negative effect of the difference in the statutory corporate tax rate of a subsidiary to
its parent on the unconsolidated pre–tax profits of this subsidiary, while controlling for
a range of firm and country variables. Our preferred specification give evidence that
pre–tax profits decrease by 7% if the tax differential increases by 10 percentage points.
Various robustness tests support our profit shifting inference.
In addition, our results suggest an overall shift of profits out of the EU. We conclude
this from a separate analysis of subsidiaries located in a high tax country (relative
to the tax rate of their parent) and, vice versa, of subsidiaries located in a low tax
country relative to their parent’s tax rate. The shifting intensity of firms in high tax
countries turns out to be about three times larger than for firms in low tax countries.
One explanation for this net shifting of profits out of the EU could be the higher
proportion of tax havens outside the EU, as about one third of the subsidiaries in our
sample exhibit a Non–EU–parent.
Finally, we provide evidence that a higher parent’s ownership share of a subsidiary
leads to an increase in the level of profit shifting between these two affiliates and vice
versa. Empirical evidence for the positive impact of the ownership share on the intensity
of shifting is, to the best of our knowledge, missing in the existing literature.
The remainder of the paper is organized as follows. In Section 2, a simple model
of profit shifting is extended, taking account of the effects of the parent’s ownership
share of its subsidiary on the level of profit shifting. From this model we derive the
hypotheses for the econometric specifications. Section 3 describes and discusses the
data and the composition of the main firm sample used in the empirical analysis.
Section 4 presents the estimation approach, the empirical results (Section 4.1), a range
of robustness checks (Section 4.2) and an extensions to empirically analyze the influence
of the ownership share on the shifting intensity (Section 4.3). Section 5 concludes.
2 A simple model of profit shifting
We set up a simple theoretical model to derive our testable hypotheses. For this purpose
we relate to Grubert (2003) and include a parameter for the ownership share, as first
introduced by Weichenrieder (2007). We assume a MNE with some degree of market
power and with one foreign subsidiary. The parent, firm 1, has to bear the statutory
4
profit tax rate 0 ≤ t1 < 1, the subsidiary has to bear 0 ≤ t2 < 1. The parent and the
subsidiary engage in exogenous intra–company transactions T , i.e. they purchase and
sell a given amount of intermediate products or intra–company services. This provides
the MNE with the opportunity to shift profits by deviating from the arm’s length price
for these intra–company sales.
Profit shifting is modeled through the shifting parameter s which represents the
amount of profit shifted per transaction. In the tax scenario t1 > t2, profits are shifted
from the parent to the subsidiary, i.e. s > 0, and a higher s means more shifting. The
opposite is true for a reversed tax scenario. The parameter ρ represents all expected
costs of shifting, including the probability of detection, the penalty, image loss, costs
of distorted management incentives, etc.
The parameter 0 < δ ≤ 1 denotes the parent’s share of ownership of its subsidiary,
with δ = 1 indicating a wholly owned affiliate. We incorporate a major effect that the
parent’s ownership share of its foreign subsidiary can have on the level of profits shifted
from or to this affiliate. A lower ownership share results in a limited enforceability
of profit shifting strategies due to opposed management interests from other parties
involved. We call this the management effect of the ownership share on the shifting
intensity and model this effect by an increase in the costs of shifting if the ownership
share declines.
The output of the parent, F1(K1, L1), is produced with mobile capital and immobile
labor with cost r and w1, respectively. We assume no depreciation of capital. The
deductibility of the tax penalty is fully embodied in ρ. Hence, the after–tax profit of
the parent is given by
πnet1 = (1− t1) [F1(K1, L1)− rK1 − w1L1 − s T ]
− (ρ/2)(s/δ)2 T (1)
The last term gives a quadratic specification of the expected shifting costs which is
frequently used in the literature. We also assume that these costs are solely borne by
the parent.6 The respective after–tax profit of the subsidiary is
πnet2 = (1− t2) [F2(K2, L2)− rK2 − w2L2 + s T ] (2)
6We do not assume any effect of the ownership share on the probability of detection, i.e. we expect
that a parent with a partly owned affiliate (e.g. 51% of the shares) is treated equal by the tax authority
as if the affiliate is wholly owned (same intensity of investigation and penalty).
5
Summing up these two affiliates’ profits yields the overall after–tax profit of the MNE:7
Πnet = (1− t1) [F1(K1, L1)− rK1 − w1L1]
+ (1− t2) [F2(K2, L2)− rK2 − w2L2]
+ (t1 − t2)s T − (ρ/2)(s/δ)2 T (3)
The shifting term (first term in the last row of Eq. (3)) reflects the tax gain from
shifting profits (before penalties). Maximizing overall net profits of the MNE holding
all input factors fixed yields the optimal level of shifting:
s∗ =(t1 − t2) δ 2
ρ(4)
Thus, in the optimum, s∗ > 0 if t1 > t2, i.e. the MNE shifts profits from the parent to
the subsidiary. Vice versa, s∗ < 0 if t1 < t2 and profits are shifted to the parent. The
optimal level of shifting has the following comparative static properties:
∂s∗
∂(t1 − t2)> 0 (5)
∂s∗
∂δ=
(t1 − t2) 2 δ
ρ> 0 if t1 > t2
< 0 if t1 < t2 (6)
The optimal level of shifting increases with the tax differential (and decreases with the
expected cost of shifting parameter ρ). A rise in the ownership share δ intensifies the
optimal level of shifting s∗ via the management effect, independent of the direction of
shifting. We test this hypothesis from Eq. (6) empirically in Section 4.3. If t1 > t2,
profits are shifted to the subsidiary and thus, s∗ > 0. Hence, an increase in δ leads to a
higher level of shifting. The same results if t1 < t2, as profits are shifted to the parent
and s∗ < 0.
Holding again all inputs fixed, we get our main theoretical hypothesis which will be
tested throughout our econometric analysis to find indirect evidence for profit shifting:
∂πgross1
∂(t1 − t2)=
(∂πgross
1
∂s∗
) (∂s∗
∂(t1 − t2)
)= −T (1 + t1 − t2)
(∂s∗
∂(t1 − t2)
)< 0 (7)
7We do not model the relevance of the ownership share for the overall profit of the MNE, i.e. we
do not incorporate the effect, that a lower ownership share gives the incentive to shift profits from the
partly owned affiliate to the (wholly owned) parent and vice versa, as this effect is independent of tax
differences between these two affiliated companies. In our analysis we want to focus on profit shifting
resulting solely from tax differentials. See Grubert (2003) for a similar procedure.
6
In the tax scenario t1 > t2 (tax scenario t1 < t2), a rise (decline, i.e. getting more
negative) in the tax differential results in a higher level of shifting and yields a decrease
(increase) in the gross profit of the parent (and finally, a rise in the overall net profit of
the MNE). In addition, a rise in the ownership share leads to more shifting (Eq. (6))
which further reduces (increases) the pre–tax profit of the parent.
3 Data description and sample construction
The paper uses the European micro database AMADEUS provided by Bureau van
Dijk which contains standardized annual accounts for up to 14 years, consolidated
and unconsolidated, for up to 1.6 million companies in 38 European countries. The
database involves detailed descriptive information, numerous balance sheet and profit
& loss account items as well as information on the ownership structure.
There is no legal commitment for firms to give out information for the database.
However, the real source of the AMADEUS data is ’Creditreform’. The purpose of this
institution8 insures a strong incentive for firms to participate and additionally insures
an adequate quality of the data.9 However, a general selection problem remains. Firms
can self–select into the sample or stay out. By assuming that (more intransparent)
firms which refuse the inclusion in the database, are more willing to engage in (illegal)
profit shifting activities, our estimation results should be biased downwards. Thus, with
significant coefficients evidence for profit shifting can still be made.
The unbalanced sample we use in this paper consists of a cross–section of 22991 MNEs
from 24 EU member states (enlarged EU–25 without Malta, ’EU–24’ in the remainder)
for the time period of 1995–2005. In the construction of our sample, we consider a
firm in Europe to be multinational if there exists a corporate immediate shareholder
with totally at least 90% of the ownership shares (parent) located in a foreign country
worldwide. In this baseline sample, 72% of the affiliates own no subsidiaries, 19%
own solely domestic subsidiaries, and 9% exhibit at least one wholly owned foreign
8’Creditreform International’ traces active commercial enterprises worldwide and checks their cred-
itworthiness to provide credit reports and debt collection services to creditors.
9As in reality the calculation of arm’s length prices for transfer pricing auditing of the authorities
is difficult, time intensive and sometimes even impossible, other methods are usually applied. Mostly,
this is the so called ’Transaction Based Net Margin Method’, which compares the net margin of the
respective affiliate with similar firms of the same branch with the help of a database. For this, both
sides, many transfer pricing consultants (e.g. Deloitte) as well as more and more tax authorities (e.g.
Germany and France), use the AMADEUS database.
7
Variable Description Mean Std. Dev Min Max
Profit (Loss) before Taxation in thousand US-$, current prices 4215.01 166500.7 -7498068 52000000
Fixed Assets in thousand US-$, current prices 52373.2 571304.9 0 77400000
Cost of Employees in thousand US-$, current prices 11619.8 891456.7 0 2,94E+08
Employees 188.15 812.64 1 62784
Financial Leverage Ratio = (Total Liabilities / Total Assets) 0.6207 0.2644 0 1
Direct Ownership Share of the Parent Parent = Immediate Shareholder 0.9298 0.1606 0.3347 1
STR of the Subsidiary STR = statutory tax rate 0.3474 0.0873 0.1000 0.5676
STR of the Parent 0.3545 0.0787 0 0.5676
Difference in STR = (Subs.STR - ParentSTR) -0.0067 0.1145 -0.4676 0.4676
Dummy High Tax Country (D-HTC) =1 if (Difference in STR)>0 0.4677 0.4990 0 1
Dummy Low Tax Country (D-LTC) =1 if (Difference in STR)<0 0.5055 0.5000 0 1
Difference in STR, if D-HTC=1 0.0887 0.0756 0.0001 0.4676
Difference in STR, if D-LTC=1 -0.0899 0.0735 -0.4676 -0.0001
GDP in billion US-$, current prices 1047.7 820.96 3.76 2791.7
GDP per Capita in PPP, EU-25=100 107.32 21.51 29.70 247.50
GDP Real Growth Rate in %, relative to previous year 2.79 1.83 -1.50 16.00
Globalization Index from KOF Swiss Econ. Institute 84.69 6.81 42.02 93.21
Corruption Index Corruption Perceptions Index 7.54 1.58 2.70 10.00(CPI) from Transparency Intern.
Table 1: Descriptive Statistics
subsidiary.10 Moreover, 30% of the firms in this MNE–sample are owned by a parent
company that is located outside of the EU–24. Thus, for 70% of the cases there exists
an immediate EU–parent, which, however, does not exclude the possibility that the
global ultimate owner of these MNEs is located outside of the EU–24.
The descriptive statistics of the micro and macro data are presented in Table 1.11
The mean of the statutory tax rate (STR) of a subsidiary and of a parent is nearly
35%, respectively. Thus, the mean of the difference of both STRs is almost zero (-
0.67%), with a standard deviation of 11.5 percentage points. Compared to the STR of
the respective parent, 46.8% of our firms are located in a high tax country, 50.6% in
a low tax country, and for 2.6% of our firms both STRs are equal. For the ’high (low)
10The average number of subsidiaries in the entire sample is 1.1, in the group of affiliates with solely
domestic subsidiaries is 2.5, in the group of affiliates with at least one foreign subsidiary is 6.4.
11Data on GDP per capita and GDP real growth rate comes from the European Statisti-
cal Office (Eurostat), data on GDP comes from the IMF as Eurostat solely provides data in
the eurocurrency. Data of the globalization index is from the KOF Swiss Economic Institute
(http://globalization.kof.ethz.ch), data of the corruption index (Corruption Perceptions Index – CPI)
is from ’Transparency International’ (http://www.icgg.org/corruption.cpi.html).
8
tax country firms’ the average difference in the STR is about 9% (-9%).
4 Empirical analysis
In this section, we start with discussing the estimation approach and the baseline
specification. Then, Section 4.1 presents our main empirical results for a panel and a
cross–section study. In Section 4.2, a range of robustness checks are undertaken. In an
extension in Section 4.3, the influence of the ownership share on the shifting intensity
is empirically analyzed.
The applied specification is the regression of unconsolidated pre–tax profits12 of
profit–making subsidiaries of MNEs on various firm and country characteristics and
additionally on the statutory corporate tax rate differential. This bilateral differential
is the difference of the STR of an affiliated company in the EU–24 to the STR of its
worldwide immediate foreign shareholder who owns at least 90% of the shares (parent).
Taking tax differentials is a more precise procedure in capturing the extent of the
profit shifting incentive for a MNE than just working with STRs of the subsidiary.
Quantitative interpretations of purely tax rate coefficients have to be taken with care.
Calculated tax rate effects on pre–tax profits might not be confined to profit shifting
activities, as the incentive to invest in a given country also decreases with the corporate
tax rate.13
Thus, based on our main hypothesis from Eq. (7) of the theoretical model14, the
econometric model for the empirical analysis is
PBTit = β0 + β1Xit + β2Yjt + δDIFFSTRit + µzi + ϕvj + εijt (8)
with subindex i denoting the observational unit on the micro level, i.e. a subsidiary,
and subindex j denoting the macro level, i.e. a member state of the EU–24. Subindex
12Pre–tax profits are taken form the balance sheet item ’Profit (Loss) before Taxation’ which is net
of all costs but before taxation. Using EBIT, which includes interest payments, as dependent variable
would blur the effect of the tax differential as these payments can also serve as a profit shifting channel.
13By constructing the tax differential, the statutory tax rate is the relevant tax measure for an
analysis of profit shifting (see e.g. Devereux (2006)). Furthermore, a MNE can define its own tax base
by the shifting of profits. Thus, using effective (marginal or average) tax rates instead of statutory
rates would be misleading in this application. We will discuss this general identification problem of
the tax rate further at the end of Section 4.2.
14Note that to confer the theoretical model on our empirical specification the status of the parent
and of the subsidiary have to switch. Hence, from the perspective of the subsidiary, the cost of shifting
term is irrelevant which further simplifies the derivative in Eq. (7).
9
t indicates the time period. PBTit is the log of profit before taxation and Xit is a
vector of firm characteristics. These control variables on the micro level are fixed assets
as a proxy for the installed capital, the cost of employees as a proxy for the use of
labor, and the financial leverage ratio. All firm variables are calculated per employee to
control for economies of scale and are transformed in logarithmic form. Yjt is a vector
of country characteristics. These control variables on the macro level are GDP, GDP
per capita, GDP growth rate, and indices for the degree of globalization and corruption
of a country. DIFFSTRit stands for the statutory tax rate differential of affiliate i in
year t to its foreign parent. Unobserved variables on the firm level are represented by
zi and on the country level by vj, the error term is denoted by εijt. We will include
industry and country dummy variables in the cross–section analysis and year dummies
in the panel regressions.
The panel structure of our sample allows the application of fixed–effect methods
(FE) on the micro level which alleviates the serious problem of unobservable firm–
specific factors in explaining variations in profits, e.g. management quality or special
product popularity. Unfortunately, as data on the ownership structure is, till now, only
available for the cross–section of the year 2004, the last reported date in most cases,
an additional assumption has to be made to get the advantage of the FE model. We
have to assume that the country of a firm’s immediate shareholder has not changed
within the time period of 1995–2005 and is the same as in the year 2004. Additionally,
we have to assume that a parent’s ownership share does not fall below 90% throughout
this period.15
4.1 Empirical results
The first set of regressions is shown in Table 2 and 3. Table 2 displays the FE re-
gressions for the years 1995–2005 of our baseline specification from Eq. (8) for up to
13741 subsidiaries with on average 4.6 observations per firm. The log of fixed assets
per employee and the log of costs per employee are throughout highly significant at
the 1%–level. The coefficients of these firm variables are quite stable in all different
specifications and sum to about 0.64 which would denote decreasing returns to scale.
The contribution of labor to pre–tax profits is about four times higher than that of
capital and an increase in the labor input of 1% leads to a rise in pre–tax profits of
0.51%. Controlling for fixed firm effects we can explain about 75% of the variation of
15We have compared our 2004 ownership data with that of the year 1998 and found that for 87%
of our subsidiaries the country of the immediate shareholder is the same for both years.
10
Dep. Var: PROFIT BEFORE TAXATION
(1) (2) (3) (4) (5)
FIXED ASSETS 0.1169*** 0.1167*** 0.1213*** 0.1341*** 0.1389***(11.16) (11.14) (10.90) (12.59) (12.20)
COST of EMPLOYEES 0.5050*** 0.5071*** 0.5041*** 0.5084*** 0.5139***(23.83) (23.78) (22.83) (22.50) (21.68)
financial leverage ratio -1.3370*** -1.3489***(-26.32) (-25.17)
difference stat. tax rate -0.7504*** -0.7824*** -0.6513*** -0.8273*** -0.6900***(-4.78) (-5.01) (-4.07) (-5.26) (-4.32)
GDP -0.5122*** -0.2956*** -0.5479***(-4.32) (-2.76) (-5.04)
GDP per CAPITA 0.7933***(3.32)
gdp growth rate 0.0228*** 0.0160**(3.43) (2.40)
GLOBALIZATION INDEX 2.0953*** 2.0885***(5.22) (5.18)
CORRUPTION INDEX 0.1503** 0.2430***(2.11) (3.38)
Year dummies Yes Yes Yes Yes YesObservations 66045 66045 61226 60929 56374Firms 13741 13741 13389 13208 12855Avg. years per firm 4.8 4.8 4.6 4.6 4.4Countries 24 24 24 24 24Adjusted R² 0.7371 0.7372 0.7431 0.7533 0.7594
Note:Variables in capital letters calculated in logarithmic term. All firm variables calculated per employee and in logarithmic term, besides of the leverage ratio. t-values in parentheses based on robust standard errors adjusted for firm clusters. *,**,*** = significance at the 10%, 5%, 1% level. Adjusted R² values calculated from a dummy variables regression equivalent to the fixed-effects model.
Table 2: Indirect Evidence of Profit ShiftingFixed-Effects, panel 1995-2005
profits before taxation.16
The influence of the tax difference is consistently negative and highly significant
which confirms our theoretical expectations from Eq. (7) and, thus, gives indirect evi-
dence for profit shifting. On average, our results suggest a decrease in pre–tax profits
of 7.4% if the statutory tax rate difference of an affiliated firm to its parent increases
by 10 percentage points. Our most preferred specification, regression (5) of Table 2,
yields a coefficient of the tax differential of 0.69. All time–constant country effects are
controlled for by the FE approach, as these country effects are the same for all firms
of a country. However, to control for time–varying country effects, we include GDP as
a proxy for the market size and GDP per capita, both in logarithmic terms, in spec-
ification (2). Surprisingly, the coefficient of GDP turns out significantly negative. An
explanation could be that big markets are characterized by a high degree of competi-
tion which results in lower profits. GDP per capita as a proxy for the overall economic
16The presented adjusted R2 values of all FE regressions done in this paper are calculated from a
dummy variables regression equivalent to the FE model.
11
development of a country has a significantly positive influence on firm profits which
indicates that richer countries possess the more advanced technologies with a more
profitable production. The inclusion of country or industry dummy variables is not
feasible in FE regressions as these variables do not change over time.
To control for further macro effects which are not constant over time, in specification
(3) we additionally include the GDP growth rate as a proxy for the economic situation
and (logarithmized) indices for the degree of globalization and corruption as proxies
for the institutional framework of a country.17 All of these variables show a positive
and significant effect. In specification (4) and (5) we incorporate the financial leverage
ratio of a subsidiary which results in a highly significant and large negative coefficient
of -1.3. This is not surprising as the balance sheet item ’profit (loss) before taxation’
is minus all deductible costs which includes interest payments, in contrast to EBIT or
EBITDA. The coefficients of all other explainable variables stay stable and significant
and the adjusted R2 value rises to 0.76 in regression (5).
We have also run the regressions from above in an OLS cross–section for the year
2004, results are presented in Table 3. This is done for a fundamental robustness test
because of the data restrictions of the ownership information mentioned at the end of
the last section. However, with the cross–section analysis we cannot control for fixed
firm effects which consequently results in higher coefficients of the tax differential and
in a small R2 value of about 0.30, less than the half of the value from the FE regressions.
Though, all decisive effects are still significant with the right sign. In the cross–section
analysis we can now include 59 industry dummy variables (NACE Rev.1 2–digit level).
In regression (6) and (7) we leave out the macro control variables but instead include
country dummies. This inclusion is feasible as the tax differential varies across firms.
Basically, the coefficient of the tax differential decreases substantially if macro variables
or instead country dummies are included.
For the FE regressions presented in Table 4 we construct two separate samples of
subsidiaries located in a high tax country (HTC) relative to the tax rate of their parent,
and, vice versa, of subsidiaries located in a low tax country (LTC) relative to their
parent’s STR, to distinguish between respective shifting intensities. The coefficient of
the tax difference turns out to be about three times higher for firms in a HTC (-1.23
in specification (3)) than for firms in a LTC (-0.40 in (6)) which indicates a much
17We leave out GDP per capita in specification (3) because of its high correlation with the index of
globalization and with the index of corruption.
12
Dep. Var: PROFIT BEFORE TAXATION
(1) (2) (3) (4) (5) (6) (7)
FIXED ASSETS 0.2831*** 0.2825*** 0.2855*** 0.2685*** 0.2689*** 0.2853*** 0.2678***(8.05) (8.18) (8.29) (8.93) (9.06) (8.31) (9.15)
COST of EMPLOYEES 0.5940*** 0.6198*** 0.6269*** 0.6035*** 0.6377*** 0.6511*** 0.6539***(11.75) (10.01) (10.08) (11.00) (9.13) (10.15) (9.50)
financial leverage ratio -1.4086*** -1.4182*** -1.4257***(-8.77) (-8.35) (-8.13)
difference stat. tax rate -2.1127*** -1.9458*** -1.7628*** -1.5252*** -1.3136*** -1.3027*** -0.9595***(-5.07) (-7.33) (-5.88) (-3.43) (-4.64) (-5.38) (-4.19)
GDP 0.0230 0.0373* 0.0359(0.74) (1.76) (1.64)
GDP per CAPITA -0.2470*(-1.86)
gdp growth rate 0.0684** 0.0386(2.32) (1.23)
GLOBALIZATION INDEX -0.4843 0.1960(-0.83) (-0.35)
CORRUPTION INDEX 0.0101 -0.1758(0.08) (-1.19)
59 industry dummies Yes Yes Yes Yes Yes Yes YesCountry dummies No No No No No Yes YesCountries 18 18 18 18 18 18 18Observations 9104 9104 9104 8412 8412 9104 8412R² 0.2619 0.2626 0.2632 0.3092 0.3105 0.2676 0.3149
Note:Variables in capital letters calculated in logarithmic term. All firm variables calculated per employee and in logarithmic term, besides of the leverage ratio. t-values in parentheses based on robust standard errors, clustered within 18 country cells. *,**,*** = significance at the 10%, 5%, 1% level.
Table 3: Indirect Evidence of Profit ShiftingOLS, cross-section 2004
more elastic tax base in HTCs than in LTCs in Europe.18 Together with the fact
that almost the half of the firms from our main sample are located in a HTC of this
definition (see Table 1), the empirical findings suggest that the net effect of profits
shifted in and out of the EU–24 is negative. Overall, profits seems to be shifted out
of the EU–24. One explanation could be the higher appearance of tax havens outside
of the EU because 31.2% (29.1%) of our HTC–firms (LTC–firms) exhibit a Non–EU–
parent. These Non–EU–parents are potentially located in tax havens and shift high
levels of profits from their EU–subsidiaries to this tax haven. Otherwise, some EU–
parents additionally have Non–EU–subsidiaries which could be located in tax havens
outside of the EU. These parents shift profits possibly increasingly or solely to their
tax–haven–subsidiaries although their EU–subsidiaries (our observational units) are
LTC–firms.
18Note that for firms in a HTC (LTC) the difference of the STR to its parent is positive (negative).
13
Dep. Var: PROFIT BEFORE TAXATION
(1) HTC (2) HTC (3) HTC (4) LTC (5) LTC (6) LTC
FIXED ASSETS 0.1493*** 0.1485*** 0.1667*** 0.0780*** 0.0774*** 0.1045***(7.54) (7.52) (7.63) (6.50) (6.44) (7.99)
COST of EMPLOYEES 0.5976*** 0.6016*** 0.5955*** 0.4539*** 0.4531*** 0.4577***(14.93) (15.01) (13.58) (17.15) (17.07) (15.15)
financial leverage ratio -1.4243*** -1.2558***(-15.16) (-18.24)
difference stat. tax rate -1.5273*** -1.5185*** -1.2337*** -0.4661** -0.4653** -0.3990*(-4.18) (-4.21) (-3.34) (-2.10) (-2.09) (-1.72)
GDP -0.6521*** -0.6660*** -0.3894*** -0.3983***(-2.83) (-3.08) (-2.49) (-2.75)
GDP per CAPITA -0.5677 1.1724***(-1.30) (3.57)
gdp growth rate 0.0107 0.0101(0.87) (1.15)
GLOBALIZATION INDEX 0.3566 3.3728***(-0.48) (6.27)
CORRUPTION INDEX 0.3175*** 0.1868(-3.00) (1.61)
Year dummies Yes Yes Yes Yes Yes YesObservations 25948 25948 22872 37676 37676 31659Firms 6450 6450 5959 9077 9077 8248Avg. years per firm 4.0 4.0 3.8 4.2 4.2 3.8Countries 24 24 24 24 24 24Adjusted R² 0.7577 0.7579 0.7733 0.7509 0.7511 0.7735
Note:Regressions (1)-(3) done solely with affiliates in high tax countries (HTC) regarding their parent's tax rate, regressions (4)-(6) done solely with affiliates in low tax countries (LTC). Variables in capital letters calculated in logarithmic term. All firm variables calculated per employee and in logarithmic term, besides of the leverage ratio. t-values in parentheses based on robust standard errors adjusted for firm clusters. *,**,*** = significance at the 10%, 5%, 1% level. Adjusted R² values calculated from a dummy variables regression equivalent to the fixed-effects model.
Table 4: Profit Shifting in High Tax vs. Low Tax CountriesFixed-Effects, panel 1995-2005
4.2 Robustness checks
At first, we vary the method of normalization. Instead of dividing by the number
of employees, we alternatively calculate our firm variables in ratios of operating rev-
enue/turnover and of total assets. In both FE regressions analogous to specification
(5) of Table 2 we get the same qualitative results of all explainable variables but a
smaller coefficient of the tax differential of -0.54. A firm’s number of employees is likely
to be less influenced by tax rates than monetary values which makes a division by the
number of employees a more suitable way to control for economies of scale.19 Instead
of using the cost of employees as a proxy for the labor input, we alternatively took the
number of employees. Again, we find no significant change in our results and obtain a
slightly larger coefficient of the tax differential of -0.65.
The fact that some firms in our sample have the same parent could possibly bias the
19Without any normalization, our results again do not change qualitatively. The effect of the tax
differential is very similar, -0.65, and the adjusted R2 value rises to 0.84.
14
standard errors or overestimate the effect of the tax differential. To manage this prob-
lem, we randomly deleted observations with a duplicated firm–parent–interconnection
to exclusively keep firms with unique parents in the sample. With 11421 subsidiaries
this sub–sample reduces to roughly the half of our original sample. All repeated re-
gressions of Table 2 and 3 yield very similar quantitative results. The coefficient of the
tax differential turns out to be even slightly larger (-0.75), compared to our preferred
specification (5) of Table 2, and again significant at the 1%–level.
A fundamental robustness check to confirm our profit shifting inference is to compare
the tax rate elasticities of MNEs with those of purely nationally organized companies
(cf. Mintz and Smart (2004)). In such an analysis, we obviously have to revert to the
STR as tax rate differences to a foreign parent do not exist for Non–MNEs. In the
first approach, we construct a separate sample of nearly 110000 individual domestic
firms with no shareholders and no subsidiaries (Domestic–sample) to compare these
regressions with those done with our initial MNE–sample.20 These regressions are shown
in Table A1 in the Appendix. For the purpose of a qualitative robustness test, in this
comparison and in the following, we focus on cross–section analysis for the year 2004
with OLS to get undoubted ownership information necessary for correctly separating a
multinational from a domestic company.21 It is inappropriate to include country dummy
variables additionally to the STR as then, the effect of the STR would not be identified.
We find no significance of the STR with the Domestic–sample (regressions (1)–(3)) but
highly significant negative coefficients of the STR with the MNE–sample (regressions
(4)–(6)). This underlines the relevance of the STR for companies with at least one
connection to a foreign country via its shareholder or via one or more subsidiaries in
explaining variations in pre–tax profits. This in turn supports our prior profit shifting
findings.22
In the second approach, we merge two samples and do cross–section regressions
for the year 2004 on the whole sample of 77591 firms. The first sample (NonMNE–
sample, 70% of all firms) contains firms with a purely national structure, i.e. they
are all owned by a domestic shareholder (with at least 90% of the shares) and also
20For this analysis, our initial MNE–sample is broadened by firms with no or no foreign immediate
shareholder but with at least one foreign subsidiary owned with minimum 90% of the shares.
21See the beginning of Section 4 for a discussion on the ownership data restrictions.
22Specification (3) and (6), respectively, of Table 5 are added because of a high correlation between
the STR and GDP and between the STR and GDP growth rate in the Domestic–sample which could
result in an insignificance of the respective coefficients due to multicollinearity problems.
15
have solely domestic subsidiaries, if any.23 The second sample (MNE–sample, 30%
of all firms) consists of our initial MNE–sample used in the regressions of Table 2
and 3. These two samples contain firms which all are very similar and comparable
in their ownership structure but with the fundamental difference that the NonMNEs
have no direct access to foreign countries and therefore cannot exploit differences in
international STRs to shift profits. According to this, we set the difference in the STR
of a firm to its parent equal to zero for all NonMNE–firms. Regression results are
presented in column (1)–(5) of Table A2 in the Appendix. The coefficient of the MNE–
dummy suggests that MNEs make significant higher pre–tax profits per employee than
national structured firms. The impact of our designed tax differential on profits before
taxation is significantly negative despite of the large share of NonMNEs in the whole
sample whose tax differential is equal to zero. In regressions (3)–(5) the STR enters
additionally to capture the tax rate effect segregated. The STR shows a significant
and large negative effect which reduces the coefficient of the tax differential. However,
the supplemental effect of the tax differential remains negative and significant which
supports our initial qualitative results on the profit shifting evidence.
Finally, in regressions (6) and (7) of Table A2 in the Appendix we solely use our
MNE–sample and additionally control for the STR. The two specifications are identical
to (1) and (5) of Table 3, only the STR is appended. This last robustness check mitigates
the potential identification problem discussed in the next paragraph. The effect of the
tax differential, which gives MNEs the incentive to shift profits, is still negative and
significant at the 1%–level. The cross–section results for the year 2004 suggest that a
decrease in the STR of a firm’s foreign parent of 10 percentage points leads to a drop
in the firm’s reported pre–tax profits of nearly 10%, other things being equal.
The empirical literature on indirect evidence of profit shifting potentially suffers
from a general identification problem of the STR effect on pre–tax profitability, as
mentioned recently by Becker and Fuest (2007). They argue that high tax countries
are likely to attract investment projects which yield low profits and contribute little
to the host countries’ corporate tax revenue whereas low tax countries can expect
’good’ investments with high profits. They call this the composition effect of corporate
taxation on foreign direct investment. Potentially, the empirical effect of the STR on
pre–tax profits does not represent profit shifting but rather the result of the selection
process in the ’portfolio–decision’ of the MNE where to locate their foreign subsidiaries,
as suggested by Becker and Fuest (2007). Devereux (2006) discusses this identification
2380% of these NonMNEs have no subsidiaries and 20% own one or more domestic subsidiaries.
16
problem and gives a comprehensive literature overview (e.g. p. 12-13 or p. 25-26).
He argues that the location decision conditional on producing abroad (the incentive to
invest in a foreign country) is predominantly influenced by the effective average tax rate
(EATR) and that the optimal level of investment conditional on the location should
be determined by the effective marginal tax rate (EMTR). The decision of location is,
e.g., analyzed by Devereux and Griffith (1998) who provide evidence that the EATR
is highly significant in this discrete decision (see also Stowhase (2002)). At the final
stage, the location of profits should be determined mainly by the STR or, in the more
strict sense, by the STR differential, as used in our paper.24
4.3 Influence of the parent’s ownership share
The theoretically stimulating effect of the parent’s ownership share of its subsidiary
on the level of shifting is described in Section 2. An increase in the ownership share
leads to a boost in profit shifting activities via the management effect as the feasibility
of implementing more shifting strategies improves due to more share voting rights or
more management influence at the subsidiary.
To test this hypothesis from Eq. (6) empirically, we construct a new sample of MNEs
with the same criteria as our initial MNE–sample except for the fact that the parent’s
minimum direct ownership share of its foreign subsidiary is relaxed to 25%. The average
ownership share in this new sample is 93% with a standard deviation of 16 (see Table
1). To capture the additional effect of the ownership share we generate an interaction
term between the STR difference and the parent’s direct ownership share. Based on our
hypothesis from Eq. (6) in combination with our indirect approach of profit shifting
evidence from Eq. (7), we would expect a significantly negative coefficient of this inter-
action term to represent more shifting via an additional impact of the tax differential
on pre–tax profits for a higher ownership share.
Regression results of a cross–section analysis for the year 2004 with OLS are shown
24The endogenous choice of a MNE to establish a foreign subsidiary is incorporated in the empirical
analysis of Egger, Eggert, and Winner (2007). They control for this bias of self–selection by applying
the procedure of propensity score matching. Hence, they can provide strong evidence that foreign–
owned subsidiaries exhibit about half as much profit tax payments than affiliates with solely domestic
owners, using also the AMADEUS database. Our paper proposes profit shifting as one of the main
reasons for this gap in tax payments.
17
Dep. Var: PROFIT BEFORE TAXATION
(1) (2) (3) (4) (5) (6)
FIXED ASSETS 0.2477*** 0.2485*** 0.2738*** 0.2725*** 0.2477*** 0.2485***(9.72) (9.76) (8.70) (8.42) (9.72) (9.76)
COST of EMPLOYEES 0.6460*** 0.6541*** 0.6755*** 0.6538*** 0.6460*** 0.6541***(9.12) (9.36) (9.78) (12.81) (9.12) (9.36)
financial leverage ratio -1.4799*** -1.4739*** -1.4799*** -1.4739***(-8.14) (-8.13) (-8.14) (-8.13)
statutory tax rate -2.1246* -2.5427*** -2.1246*(-1.83) (-2.96) (-1.83)
difference stat. tax rate -1.1852*** -0.7518*** -1.0691*** -1.1768*** 1.7904 2.1906(-5.06) (-3.10) (-4.73) (-4.71) (1.16) (1.62)
interact.(diff.STR)x(Ownership- -3.2003* -3.1645** -3.5380*** -3.1657*** MeanOwnership) (-2.00) (-2.48) (-3.15) (-3.26)
interact.(diff.STR)x(Ownership) -3.2003* -3.1645**(-2.00) (-2.48)
parent's ownership share 0.1734 0.1288 -0.0997 0.0800 0.1734 0.1288(1.41) (1.16) (-0.88) (0.63) (1.41) (1.16)
Macro Control Variables Yes Yes No No Yes Yes59 industry dummies Yes Yes Yes Yes Yes YesCountry dummies No No Yes No No NoCountries 17 17 17 17 17 17Observations 8107 8107 8815 8815 8107 8107R² 0.3165 0.3173 0.2731 0.2667 0.3165 0.3173
Note:'interact.(diff.STR)x(Ownership-MeanOwnership)' is the interaction term between the difference in the statutory tax rates and the deviation of the parent's direct ownership share from its mean (correlation of these two variables is 12.8%). 'interact.(diff.STR)x (Ownership)' is the interaction between the tax difference and the ownership share (correlation of these two variables is 98.95%). Macro control variables are: GDP, gdp growth rate, GLOBALIZATION and CORRUPTION INDEX. Variables in capital letters calcu- lated in logarithmic term. All firm variables calculated per employee and in logarithmic term, besides of the leverage ratio. t-values in parentheses based on robust standard errors, clustered within 18 country cells. *,**,*** = significance at the 10%, 5%, 1% level.
Table 5: Parent's Ownership Share and Profit ShiftingOLS, cross-section 2004
in Table 5.25 The familiar control variables on the macro level are not listed. Due to the
large number of firms with a parent that fully owns the affiliate (73% of the firms), the
tax differential and an interaction term with the ownership share is highly correlated
(98.95%) which reduces the significance of both coefficients due to multicollinearity
reasons (see regressions (5) and (6) in Table 5). The interpretation of such an interaction
effect is also not straightforward. For this reason, to get a more clear-cut interpretation,
we calculate the interaction term by multiplying the tax differential with the deviation
of the ownership share from its mean (’centering’). The correlation between these two
variables is at the moderate level of 12.8%. Results are presented in regressions (1)–
(4) of Table 5. Basically, the coefficient of the tax differential describes the effect of
the tax difference on pre–tax profits for an average ownership share of 93%. For an
ownership share above this mean, an increase in the STR difference has a stronger
25The cross–section analysis for the year 2004 is again preferred to the FE panel study due to the
data restrictions on the ownership structure addressed in the last paragraph.
18
negative impact on pre–tax profits than for ownership shares below the mean.26 Thus,
an increase in the ownership share, holding the tax differential fixed, strengthens profit
shifting in the sense that the sum of both coefficients decreases further. This is in
line with our hypotheses from Eq. (6) and (7). In specification (3) and (4) of Table
5 we leave out the leverage ratio and the macro controls but include country dummy
variables in specification (3). In these regressions the coefficient of the interaction term
is significant at the 1%–level. To conclude, our results provide (indirect) evidence that
firms with a higher parent’s ownership share are engaged in a higher level of profit
shifting.
Robust results can also be inferred without ’centering’ the ownership share in the
interaction term. Results with this interaction term are presented in regression (5)
and (6) of Table 5.27 As mentioned above, multicollinearity leads now to higher stan-
dard errors which strongly reduces the significance but leaves the estimators unbiased.
Nevertheless, the negative and large effect of the interaction term stays significant.28
5 Conclusions
The paper provides indirect evidence for profit shifting by multinationals owning sub-
sidiaries within the EU using the European micro database AMADEUS. We explain
variations in pre–tax profits of profit–making subsidiaries located in the EU with the
statutory tax rate difference of these affiliates to their respective foreign parents, while
controlling for a range of firm and country variables. Whereas the qualitative results of
our profit shifting evidence mainly come from a cross–section analysis for the year 2004,
the quantitative results are derived from a panel analysis of 1995–2005 with fixed–
effects regressions controlling for unobserved firm effects. Our regressions indicate a
26This results as the coefficient of the interaction term multiplied with the respective (positive or
negative) deviation from the ownership mean has to be added to the original coefficient of the tax
differential.
27It is evident that with this modification all coefficients of regression (5) and (6) are equal to the
respective results in regression (1) and (2), except for the tax differential.
28Now, the coefficient of the tax differential gives the effect of the tax difference on pre–tax profits
for an ownership share of zero. Thus, with an increasing ownership share the total effect of the tax
differential (the sum of both coefficients) decreases, and gets negative at an ownership share of 69%
(for regression (6)). Above this share, the effect of the tax differential on pre–tax profits is negative.
Focusing on this group, a further increase in the ownership share, holding the tax differential fixed,
intensifies profit shifting in the sense that the sum of both coefficients decreases further.
19
highly significant negative impact of the tax differential on profits before taxation. The
results suggest a decrease in pre–tax profits of about 7% if the tax rate difference of a
subsidiary to its immediate shareholder increases by 10 percentage points.
The coefficient of the tax differential of affiliates in high tax countries (relative to the
tax rate of the parent) turns out to be about three times higher than for subsidiaries
located in low tax countries. This indicates for Europe a much more elastic tax base
in high tax countries. We interpret this as an overall shift of profits out of the EU.
Furthermore, we provide empirical evidence that a higher parent’s ownership share of
a subsidiary leads to an increase in the level of shifting between these two affiliates and
vice versa. We include this ownership effect of enhanced or reduced shifting possibilities
in our simple theoretical model. Several fundamental robustness checks to confirm
our profit shifting inference were undertaken. Two comparisons of the tax effects of
MNEs with those of domestic individual enterprises and with those of purely national
organized companies support our results.
Future research should aim at identifying specific channels for profit shifting with
European micro data. For example, data on intangible assets could serve as a proxy
for R&D expenditures, which in turn could be used to provide indirect evidence of
profit shifting via the overhead costs channel. A R&D intensive MNE benefits from the
higher intransparency that came along with its products as it is much more difficult
to estimate arm’s length prices for unique (high–tech) products (e.g. pharmaceuticals,
semiconductors as well as patents, royalties or advertising). This leads to a lower prob-
ability of detection and, thus, to lower shifting costs, which should result in a higher
level of shifting. Furthermore, while empirically explaining the potential net shifting
of profits out of the EU, future research on this issue should analyze the relevance of
international tax havens.
20
6 Appendix
Dep. Var: PROFIT BEFORE TAXATION
(1) Dom. (2) Dom. (3) Dom. (4) MNEs (5) MNEs (6) MNEs
FIXED ASSETS 0.2649*** 0.2591*** 0.2600*** 0.3243*** 0.3285*** 0.3234***(14.97) (13.10) (13.10) (10.08) (10.70) (10.01)
COST of EMPLOYEES 0.7512*** 0.7859*** 0.7507*** 0.6127*** 0.6283*** 0.6317***(30.27) (18.92) (17.05) (10.27) (9.19) (9.36)
financial leverage ratio -1.5576*** -1.5921*** -1.6104*** -1.4403*** -1.4486*** -1.4479***(-7.83) (-7.37) (-7.20) (-9.88) (-9.63) (-9.69)
statutory tax rate 0.7485 2.3379 -1.0493 -2.2563*** -3.0518*** -2.3672***(0.61) (1.08) (-0.79) (-2.78) (-8.16) (-2.81)
GDP -0.0240* 0.0936***(-1.99) (7.05)
GDP per CAPITA 0.7268 0.0077(1.45) (0.03)
gdp growth rate -0.1112 0.0277(-1.11) (1.55)
GLOBALIZATION INDEX 0.4132 -0.0215 -0.3658* 0.1172(0.41) (-0.02) (-1.83) (0.12)
CORRUPTION INDEX -0.0154 -0.5229* -0.1341* -0.1897(-0.05) (-1.76) (-1.76) (-1.57)
59 industry dummies Yes Yes Yes Yes Yes YesCountries 18 18 18 18 18 18Observations 108646 108646 108646 12933 12933 12933R² 0.3813 0.3847 0.3833 0.4009 0.4034 0.4014
Note:Regressions (1)-(3): Exclusively firms with no subsidiaries and no sharholders (Domestic-sample). Regressions (4)-(6): Firms with a foreign immediate shareholder or at least one foreign subsidiary (MNE-sample). The correlation between STR and GDP and between STR and GDP growth rate is 0.86, respectively, in regression (2). Variables in capital letters calculated in logarithmic term. All firm variables calculated per employee and in logarithmic term, besides of the leverage ratio. t-values in parentheses based on robust standard errors, clustered within 18 country cells. *,**,*** = significance at the 10%, 5%, 1% level.
Table A1: Robustness 1 - MNEs vs. Domestic Individual Firms (Dom.)OLS, cross-section 2004
21
Dep. Var: PROFIT BEFORE TAXATION
(1) (2) (3) (4) (5) (6) MNEs (7) MNEs
FIXED ASSETS 0.2838*** 0.2639*** 0.2870*** 0.2862*** 0.2650*** 0.2848*** 0.2695***(14.26) (17.44) (14.58) (14.53) (17.05) (8.24) (9.09)
COST of EMPLOYEES 0.7494*** 0.7351*** 0.8255*** 0.8020*** 0.7438*** 0.6284*** 0.6439***(10.69) (16.81) (11.37) (12.13) (16.76) (11.36) (9.01)
financial leverage ratio -1.5140*** -1.4989*** -1.4128***(-10.12) (-10.04) (-8.28)
statutory tax rate -3.6750*** -3.6351*** -2.6735** -2.0830** -1.6333*(-2.75) (-2.73) (-2.37) (-2.38) (-1.74)
difference stat. tax rate -2.5711*** -1.7755** -1.7409*** -1.3057** -1.3816* -1.3915*** -0.9942*** (=0 for NonMNEs) (-4.30) (-2.31) (-2.80) (-2.43) (-1.76) (-5.33) (-4.63)
GDP -0.0153 0.0111 0.0436*(-0.55) (0.53) (1.85)
gdp growth rate 0.0151 -0.0509 0.0046(0.39) (-0.93) (0.12)
GLOBALIZATION INDEX 3.4464*** 2.8477*** -0.1514(2.89) (4.13) (-0.27)
CORRUPTION INDEX -0.2691 -0.2300 -0.1848(-1.23) (-1.17) (-1.28)
dummy MNE 0.1981** 0.1619*** 0.1911*** 0.1561***(2.54) (2.87) (3.38) (2.88)
59 industry dummies Yes Yes Yes Yes Yes Yes YesCountries 18 18 18 18 18 18 18Observations 45469 41828 45469 45469 41828 9104 8412R² 0.3168 0.3628 0.3205 0.3221 0.3639 0.2635 0.3110
Note:Regressions (1)-(5): Sample consists of 2 sub-samples: 70% of the firms are in the NonMNE-sample (=firms with a purely national structure: owned by a domestic shareholder with at least 90% shares and own solely domestic subsidiaries, if any), 30% of the firms are in the MNE- sample (=original sample of MNEs used in the initial regressions of table 2-4). The difference in the STR of a subsidiary to its parent is set equal to zero for all NonMNE-firms. 'Dummy MNE' is equal to 1 if a firms is allocated to the MNE-sample and zero if allocated to the NonMNE- sample. Regressions (6) and (7): Solely the MNE-sample is used. Variables in capital letters calculated in logarithmic term. All firm variables calculated per employee and in logarithmic term, besides of the leverage ratio. t-values in parentheses based on robust standard errors, clustered within 18 country cells. *,**,*** = significance at the 10%, 5%, 1% level.
Table A2: Robustness 2 - MNEs & National Structured Firms (NonMNEs)OLS, cross-section 2004
22
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